KeyNote Speech 10th International Conference of Science, Mathematics & Technology Education Mauritius Institute of Education, Reduit, Mauritius 6 November 2019

1. Educational Data Literacy for
OnlineTeaching and Learning
The case of the Learn2Analyse Initiative
Demetrios Sampson
PhD(ElectEng) (Essex), PgDip (Essex), BEng/MEng(Elec) (DUTH), CEng
Golden Core Member IEEE Computer Society, Senior Member IEEE
Professor, Digital Systems for Learning and Education
Department of Digital Systems, University of Piraeus, Greece
KeyNote Speech
10th International Conference of Science, Mathematics &Technology Education
Mauritius Institute of Education, Reduit, Mauritius
6 November 2019

2. Educational Data Literacy
The challenge and the opportunity
Definitions
The value add proposition of the
Learn2Analyze project
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3. Educational Data Literacy
The challenge and the opportunity
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4. The challenge and the opportunity
DigitalTeaching and Learning is a key innovation for both:
University Teaching and Learning
Professional Development andVocational Training
To this end, blended and online courses are nowadays widely used to
meet the needs of pre-service higher education and vocational training
students and/or in-service professionals.
Such courses are typically supported by Course or Learning
Management Systems (CMS or LMS) which are web-based systems that
handle teaching and learning activities online.
Designing, delivering, evaluating and re-designing high quality online or
blended courses and learning experiences is at the core of online
education and training.
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5. The challenge and the opportunity
Designing, delivering, evaluating and re-designing high quality online or
blended courses and learning experiences is at the core of online
education and training.
In this context, two important professional roles in the digital education
and training job market, namely:
the Instructional Designers, who design and develop online (and
blended) courses, and
the Trainers or Tutors who support the delivery of these online
(and blended) courses,
require new professional competences compared to those assumed at
the traditional face to face education and training programs.
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6. The challenge and the opportunity
A recent advancement in online and blended teaching and learning is
Educational Data Analytics (EDA): the use of educational data generated
during teaching and learning (including assessment) to better support individual
learners' in online and blended courses.
As a result, most Course Management Systems are now incorporating
Educational Data Analytics tools.
However, these tools are not widely used mainly because of the low
Educational Data Literacy (EDL) competences of the professionals
that could be using them:
instructional designers and trainers,
or even K12 teachers adopting the flipped classroom model in their
teaching
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7. The challenge and the opportunity
Educational Data Literacy is a core competence for all education
professionals, including school teachers, instructional designers and tutors
of online and blended learning course, as well as educational institutions'
leaders.
Nevertheless,
existing professional competence frameworks for educators
pay little attention to EDL, missing out the potential of using emerging
EDL methods and tools in online and blended teaching and learning -
thus there is a need for extending existing professional competence
frameworks for educators with new competences to accommodate the
emerging field of Educational Data Literacy;
there are very limited professional development courses for
cultivating EDL competences - thus there is a need for professional
development programs that develop and assess Educational Data Literacy
competences.
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8. Educational Data Literacy
Definitions
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9. Educational Data
Collected and organised to represent all aspects of teaching and learning,
including
Profiling and Interaction Data
Between
Students,Teachers, Learning Environment
derived from qualitative and quantitative methods
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10. Data Literacy for Educators
the ability to understand and use data effectively to inform educational
and pedagogical decisions
a competence set to locate, collect, analyze/understand, interpret and act
upon Educational Data from different sources so as to support
improvement of the teaching, learning and assessment process
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11. Data Literacy for Educators
Data Literacy
for Educator
Find and collect
relevant
educational data
[Data
Location]
Understand what the
educational data
represent [Data
Comprehension]
Understand what
the educational data
mean [Data
Interpretation]
Define instructional
approaches to
address problems
identified by the
educational data
[Instructional
Decision Making]
Define questions
on how to improve
practice using the
educational data
[Question
Posing]
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12. Educational Data AnalyticsTechnologies
Teaching Analytics
methods and digital tools to visualize, analyze, and/or assess teaching
practice
Learning Analytics
methods and digital tools to collect, analyze and report student-related
educational data towards monitoring the learning process
Teaching & Learning Analytics
to support the process of reflective practice: facilitating teachers to
reflect on their teaching design using evidence from the actual delivery to
their students
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13. Teaching Analytics:AnalyseTeaching Design
for self-reflection and improvement
Visualize the elements of a lesson plan
Visualize the alignment of a lesson plan to educational objectives / standards
Validates whether a lesson plan has potential inconsistencies in its design
through sharing with peers or mentors to receive feedback
Support the process of sharing a lesson plan with peers or mentors,
allowing them to provide feedback through comments and annotations
through co-designing and co-reflecting with peers
Allow peers to jointly analyze and annotate a common teaching design
in order to allow for co-reflection
mentors to receive feedback
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14. Learning Analytics
Collection of learner data during the delivery of a teaching design (e.g., a
lesson plan) to build/update individual student profiles.
Types of learner data typically are “Dynamic Student Data”:
Engagement in learning activities. For example, the progress each
learner is making in completing certain learning activities.
Performance in assessment activities. For example, formative or
summative assessment scores.
Interaction with Digital Educational Resources and Tools, for example
which educational resources each learner is viewing/using.
Emotional Data, for example stress, boredom, anxiety.
mentors to receive feedback 14

15. Educational Data AnalyticsTechnologies
Descriptive Analytics
“what has already happened”: they are related to existing data
summarization, namely the visualization of past data
Predictive Analytics
“what will happen”: they are related to processing existing data for
pattern elicitation, so as to make estimations of future trends
Prescriptive Analytics
“what should we do”: they are related to generating decision-support
recommendations for actions to be taken, based on the analysis of
existing data
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16. Educational Data Literacy
The value add proposition of the
Learn2Analyze project
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17. Learn2Analyze:
An Academia-Industry Knowledge Alliance for enhancing Online
Training Professionals’ (Instructional Designers and e-Trainers)
Competences in Educational Data Analytics
European Commission
ERASMUS+ Key Action 2 “Cooperation for innovation and the
exchange of good practices - Knowledge Alliances”
Academia – Industry - End User Communities
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18. The European Union
500 million people - 28 countries - a single market
Free movement of people, goods, services and capital
7% of theWorld's population
24% of world expenditure on research
32% of high-impact publications
32% of patent applications
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19. Erasmus+
A 14.7 Billion€ program for education, training, youth and sport
2014-2020.
1,750 Billion€ per year for Education &Training
Knowledge alliances
Structured, long-term cooperation between HEIs and enterprises
Deliver new multidisciplinary curricula responding to business
needs
Facilitate the exchange, flow and co-creation of knowledge
between HEIs and enterprises
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20. The value add proposition of the Learn2Analyze project
The scope of the Learn2Analyze project is:
to enhance existing professional competence
frameworks for instructional designers and e-trainers of online
courses with new Educational Data Literacy competences for using
emerging Educational Data Analytics methods and tools;
to develop and to evaluate a professional development Massive
Open Online Course (MOOC) for cultivating these competences with
emphasis to combining theory and practice through the use of existing
educational data analytics tools from world market leaders.
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21. The value add proposition of the Learn2Analyze project
The core outcomes of the Learn2Analyze project are:
to produce and validate a comprehensive proposal for
an Educational Data Literacy Competence Profile for instructional
designers and e-trainers of online and blended courses;
to design, develop, offer and evaluate a competence-based Professional
Development Massive Open Online Course leading to a Certificate of
Achievement in Educational Data Literacy;
to facilitate building a professional community around Educational Data
Analytics with the participants and the graduates of the Learn2Analyze
MOOC.
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22. 22

23. Educational Data Literacy Roadmap
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24. Learn2Analyze
Educational Data Literacy Competence
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25. Learn2Analyze
MOOC
Learn to Analyze Educational Data and Improve your Blended and
OnlineTeaching
Start Date: October 21th, 2019
End Date: December 14th, 2019
Pre-Requisites:None
Duration: 8 weeks
Time Commitment: 68 hours in total
Level: Introductory
Language: English
Cost: None
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26. Learn2Analyze
MOOC
Learn to Analyze Educational Data and Improve your Blended and
OnlineTeaching
Target Audience
instructional designers and e-tutors of online and blended courses
school teachers of blended learning courses (using the flipped classroom
model)
university students
Learning Outcomes
cover 100% the set of competences anticipated by the Learn2Analyze
Educational Data Literacy competence framework (L2A EDL-CP).
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27. Learn2Analyze
MOOC
Learn to Analyze Educational Data and Improve your Blended and
OnlineTeaching
The course is combines
theory on collecting, analysing, interpreting and using educational
data (including issues related with ethics and privacy),
with practice on applying educational data analytics in three different
e-learning platforms, namely, Moodle, the eXact Suite and the IMC
Learning Suite.
Leads to a free Certification of Achievement in Educational Data
Literacy upon successful completion.
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28. Learn2Analyze
MOOC
Module 1 - Orientation
Module 2 - Online and Blended
Teaching and Learning supported by
Educational Data
Topic 1 – Educational Data as a key success
factor for online and blended teaching and
learning
Topic 2 – Data is Everywhere (Educational
Data Collection)
Topic 3 – Adding value to educational
datasets (Educational Data Management)
Module 3 - Learning Analytics
Topic 1 - Using learner-generated data and
learning context for extracting learning
analytics
Topic 2 - Organizing and presenting learning
analytics
Topic 3 - Interpreting and mapping learning
analytics
Module 4 -Teaching Analytics
Topic 1 - Data sources for supporting
teaching analytics
Topic 2 - Data ethics and privacy principles
for teaching analytics
Topic 3 - Applying and communicating
educational data and analytics findings
Module 5 - Applying Educational Data
Analytics with Moodle
Topic 1 - Moodle Site Level
Topic 2 - Course Level
Topic 3 - User Level
Topic 4 - 3rd Party Tools in Moodle
Module 6 - Applying Educational Data
Analytics the eXact Suite
Topic 1 - Understanding and supporting
course progress via learning reports
Topic 2 - Additional learning reports
Topic 3 - Beyond the LMS, monitoring and
supporting informal learning via eXact
Delivery Portal
Module 7 - Applying Educational Data
Analytics the Learning Suite
Topic 1: How educational data is handled in
Learning Suite (Overview of main principles
and techniques
Topic 2: Possibilities of Teaching Analytics in
the Learning Suite)
Topic 3:Tools of Learning Analytics in the
Learning Suite
Topic 4: Using Teaching and Learning Analytics
tools of the Learning Suite to support
Teacher Inquiry
Module 8 – Concluding the MOOC
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29. http://www.learn2analyze.eu/
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